Journal: PLoS ONE

Article Title: Longitudinal analysis of developmental changes in electroencephalography patterns and sleep-wake states of the neonatal mouse

doi: 10.1371/journal.pone.0207031

Figure Lengend Snippet: ( A ) Protocol summarizing the timing of the longitudinal recording sessions in individual mice. ( B ) After electrode placement surgery at P7 or P8, the attached electrode apparatus is of minimal size and the exposed pin header allows reversible connections for recording EEG and EMG while permitting pup feeding and group nesting. ( C ) Weight gain in operated and naïve mice. *p<0.05, P14 and P17, **p<0.01, P21 naïve compared to EEG recorded mice by two-way repeated measures ANOVA with Holm-Sidak posttest; n = 9–11 per age group. (D) Histological assessment of electrode placement and injury during EEG recording of the neonatal mouse. Top panels: Cresyl violet stained coronal sections display limited injury to underlying layer I/II cortex of naive or EEG recorded (Rec) mice caused by surgery or recoding paradigm. Bottom panels: GFAP analysis shows no signs of gliosis in the cortex or hippocampus of EEG recorded (Rec) neonatal mice compared to naïve (Naïve) controls; n = 8 Rec, n = 6 Naïve. Scale bar is 500μm.

Article Snippet: EEG and EMG signals were digitized at 400Hz (MP150; Biopac) and pups were recorded in up to 3-hour sessions.

Techniques: Staining

Journal: PLoS ONE

Article Title: Longitudinal analysis of developmental changes in electroencephalography patterns and sleep-wake states of the neonatal mouse

doi: 10.1371/journal.pone.0207031

Figure Lengend Snippet: ( A ) Representative 15 second EEG and nuchal EMG traces from a postnatal day 9 (P9) mouse exhibit a discontinuous EEG pattern during periods of high muscle tone (upper traces) and muscle atonia (lower traces). A ten minute EEG/EMG CDSA displays bursts of EEG activity separated by brief periods of suppressed EEG power and limited slow wave activity. Despite intermittent bursts of EMG activity, EEG shows no definite evidence of qualitative state changes. The P9 FFT displays the mean total power within the frequency for the entire recording period. ( B ) Representative P10 EEG and EMG traces display initial evidence of state change, with low amplitude, relatively continuous EEG activity and prominent EMG activity during the awake state, higher amplitude discontinuous bursts of prominent slow wave activity on EEG and decreased EMG activity during NREM, and low amplitude continuous EEG activity with suppressed EMG with intermittent myoclonic activity during REM sleep. The CDSA displays state differences in short, defined vigilance patterns with multiple sleep/wake cycles (W- Awake; NREM- NREM sleep; REM- REM sleep; not all vigilance epochs labeled) of a P10 neonatal mouse. The P10 power FFT displays a significant increase in power (1-7Hz) during NREM labeled epochs. *p<0.05, compared to awake/REM by ANOVA with Tukey; n = 8. ( C ) Representative P12 EEG, EMG, and CDSA traces display well-defined distinction between different vigilance state patterns, with limited discontinuity during NREM sleep. The P12 power FFT displays a two-fold increase in delta power (1-4Hz) with an increase in overall power (1-17Hz) during NREM sleep episodes. *p<0.05, compared to awake/REM by Kruskal-Wallis with Dunn’s; n = 9. ( D ) Representative P14 EEG, EMG and CDSA traces display clear vigilance state patterns. The P14 power FFT displays significant increase in delta power during NREM sleep and the development of a 5Hz peak during REM sleep epochs (arrow). *p <0.05, versus awake/REM by one-way ANOVA; n = 8.

Article Snippet: EEG and EMG signals were digitized at 400Hz (MP150; Biopac) and pups were recorded in up to 3-hour sessions.

Techniques: Activity Assay, Labeling

Journal: Sleep

Article Title: Activity of a subset of vesicular GABA-transporter neurons in the ventral zona incerta anticipates sleep onset

doi: 10.1093/sleep/zsaa268

Figure Lengend Snippet: Classification of transition points between wake–sleep states. The wake–sleep states were partitioned based on EEG, EMG, EEG power spectra and video recording of the animal behavior, criteria that were same as in our previous study [19]. The transition point is indicated by the gray dashed vertical line.

Article Snippet: Data analysis of calcium traces during sleep–wake states The EEG and EMG signals that were recorded via OmniPlex® (Plexon.com) were imported into a data visualization and physiological analysis software program (Neuroexplorer.com).

Techniques:

Journal: Frontiers in Neurorobotics

Article Title: Evaluating Convolutional Neural Networks as a Method of EEG–EMG Fusion

doi: 10.3389/fnbot.2021.692183

Figure Lengend Snippet: A summary of select literature examples using CNN models with EEG or EMG signals.

Article Snippet: During data collection, the EEG and EMG signals were recorded using an Intronix 2024F Physiological Amplifier System (Intronix Technologies, Bolton, Canada).

Techniques:

Journal: Frontiers in Neurorobotics

Article Title: Evaluating Convolutional Neural Networks as a Method of EEG–EMG Fusion

doi: 10.3389/fnbot.2021.692183

Figure Lengend Snippet: The protocol followed to process the EEG/EMG signals, generate the spectrogram and signal images, and train the CNN models using different EEG–EMG fusion methods. The top path (purple) shows the steps used to develop the CNN models based on spectrogram image inputs, while the bottom path (green) shows the steps used to develop the CNN models based on signal image inputs. For all EEG–EMG-fusion-based CNN model types (represented by the final step of all paths), an EEG and EMG only version was also trained, to provide a baseline comparison for evaluating EEG–EMG Fusion.

Article Snippet: During data collection, the EEG and EMG signals were recorded using an Intronix 2024F Physiological Amplifier System (Intronix Technologies, Bolton, Canada).

Techniques: Comparison

Journal: Frontiers in Neurorobotics

Article Title: Evaluating Convolutional Neural Networks as a Method of EEG–EMG Fusion

doi: 10.3389/fnbot.2021.692183

Figure Lengend Snippet: A sample normalized spectrogram image to demonstrate the three EEG–EMG fusion methods used, where (A,B) show single-channel spectrograms and (C) visualizes a multi-channel spectrogram. (A) Shows the grouped method, where signal channels of the same type are grouped together within the image. (B) Shows the mixed method, where EEG and EMG channels are alternated to mix signal types. (C) Provides a visualization of the stacked method, where a multi-channel spectrogram is generated by combining the different EEG/EMG spectrograms in depth-wise manner.

Article Snippet: During data collection, the EEG and EMG signals were recorded using an Intronix 2024F Physiological Amplifier System (Intronix Technologies, Bolton, Canada).

Techniques: Generated

Journal: Frontiers in Neurorobotics

Article Title: Evaluating Convolutional Neural Networks as a Method of EEG–EMG Fusion

doi: 10.3389/fnbot.2021.692183

Figure Lengend Snippet: Confusion matrices, using the combined classification results for all subjects, for the single-channel spectrogram-based CNN models. (A) Shows the matrix for the grouped fusion method while (B) shows the matrix for the mixed fusion method. (C,D) Show the matrices for the EEG and EMG only models, respectively. Each matrix contains a positive/negative precision score summary in the final two rows, and a positive/negative recall score summary in the final two columns.

Article Snippet: During data collection, the EEG and EMG signals were recorded using an Intronix 2024F Physiological Amplifier System (Intronix Technologies, Bolton, Canada).

Techniques:

Journal: Frontiers in Neurorobotics

Article Title: Evaluating Convolutional Neural Networks as a Method of EEG–EMG Fusion

doi: 10.3389/fnbot.2021.692183

Figure Lengend Snippet: Confusion matrices, using the combined classification results for all subjects, for the 1D convolution signal-image-based CNN models. (A) Shows the matrix for the EEG–EMG fusion model, while (B,C) how the matrices for the EEG and EMG only models, respectively. Each matrix contains a positive/negative precision score summary in the final two rows, and a positive/negative recall score summary in the final two columns.

Article Snippet: During data collection, the EEG and EMG signals were recorded using an Intronix 2024F Physiological Amplifier System (Intronix Technologies, Bolton, Canada).

Techniques:

Journal: Frontiers in Neurorobotics

Article Title: Evaluating Convolutional Neural Networks as a Method of EEG–EMG Fusion

doi: 10.3389/fnbot.2021.692183

Figure Lengend Snippet: Confusion matrices, using the combined classification results for all subjects, for the split convolution signal-image-based CNN models. (A) Shows the matrix for the EEG–EMG fusion model, while (B,C) how the matrices for the EEG and EMG only models, respectively. Each matrix contains a positive/negative precision score summary in the final two rows, and a positive/negative recall score summary in the final two columns.

Article Snippet: During data collection, the EEG and EMG signals were recorded using an Intronix 2024F Physiological Amplifier System (Intronix Technologies, Bolton, Canada).

Techniques: